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Journal articles on the topic 'Model fuzzing'
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1
Zhu, Xue Yong, and Zhi Yong Wu. "A New Fuzzing Technique Using Niche Genetic Algorithm." Advanced Materials Research 756-759 (September 2013): 4050–58. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.4050.
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Current advanced Fuzzing technique can only implement vulnerability mining on a single vulnerable statement each time, and this paper proposes a new multi-dimension Fuzzing technique, which uses niche genetic algorithm to generate test cases and can concurrently approach double vulnerable targets with the minimum cost on the two vulnerable statements each time. For that purpose, a corresponding mathematical model and the minimum cost theorem are presented. The results of the experiment show that the efficiency of the new proposed Fuzzing technique is much better than current advanced Fuzzing techniques.
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Kim, Minho, Seongbin Park, Jino Yoon, Minsoo Kim, and Bong-Nam Noh. "File Analysis Data Auto-Creation Model For Peach Fuzzing." Journal of the Korea Institute of Information Security and Cryptology 24, no. 2 (April 30, 2014): 327–33. http://dx.doi.org/10.13089/jkiisc.2014.24.2.327.
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Song, Guang Jun, Chun Lan Zhao, and Ming Li. "Study on Software Vulnerability Dynamic Discovering System." Applied Mechanics and Materials 151 (January 2012): 673–77. http://dx.doi.org/10.4028/www.scientific.net/amm.151.673.
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Developed a new system model of software vulnerability discovering, which was based on fuzzing, feature matching of API sequences and data mining. Overcame the disadvantages of old techniques, this new method effectively improves the detection of potential unknown security vulnerabilities in software. Besides, this method is more automated and performs better in finding new security vulnerabilities.
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Guan, Quan Long, Guo Xiang Yao, Kai Bin Ni, and Mei Xiu Zhou. "Research on Fuzzing Test Data Engine for Web Vulnerability." Advanced Materials Research 211-212 (February 2011): 500–504. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.500.
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With the rapid growth of e-commerce, various types of complex applications appear in web environments. web-based system testing is different from traditional software testing. The unpredictability of Internet and web systems makes it difficult to test web-based system. This paper presents an engine for Fuzzing test data towards web control vulnerabilities, and introduces "heuristic rules" and "tagged words" to generate the test data. This method can increase the intelligence of security testing and build the foundation of web vulnerability detection model.
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Zeng, Yingpei, Mingmin Lin, Shanqing Guo, Yanzhao Shen, Tingting Cui, Ting Wu, Qiuhua Zheng, and Qiuhua Wang. "MultiFuzz: A Coverage-Based Multiparty-Protocol Fuzzer for IoT Publish/Subscribe Protocols." Sensors 20, no. 18 (September 11, 2020): 5194. http://dx.doi.org/10.3390/s20185194.
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The publish/subscribe model has gained prominence in the Internet of things (IoT) network, and both Message Queue Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP) support it. However, existing coverage-based fuzzers may miss some paths when fuzzing such publish/subscribe protocols, because they implicitly assume that there are only two parties in a protocol, which is not true now since there are three parties, i.e., the publisher, the subscriber and the broker. In this paper, we propose MultiFuzz, a new coverage-based multiparty-protocol fuzzer. First, it embeds multiple-connection information in a single input. Second, it uses a message mutation algorithm to stimulate protocol state transitions, without the need of protocol specifications. Third, it uses a new desockmulti module to feed the network messages into the program under test. desockmulti is similar to desock (Preeny), a tool widely used by the community, but it is specially designed for fuzzing and is 10x faster. We implement MultiFuzz based on AFL, and use it to fuzz two popular projects Eclipse Mosquitto and libCoAP. We reported discovered problems to the projects. In addition, we compare MultiFuzz with AFL and two state-of-the-art fuzzers, MOPT and AFLNET, and find it discovering more paths and crashes.
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Gao, Sudi, Yueying Luo, and Tan Yang. "Research on River Water Environmental Capacity Based on Triangular Fuzzy Technology." E3S Web of Conferences 236 (2021): 03018. http://dx.doi.org/10.1051/e3sconf/202123603018.
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Based on the randomness and ambiguity characteristics of the river water environment system, as well as the lack and inaccuracy of data information, the water environment system parameters are defined as triangular fuzzy numbers. On this basis, by fuzzing the parameters of the conventional deterministic model, a fuzzy model for calculating river water environmental capacity is established. According to this model, the river water environment capacity in the form of triangular fuzzy numbers can be calculated. According to the requirements of a given level of credibility, the water environment capacity can be further converted from triangular fuzzy numbers to interval values. Research shows that compared with conventional deterministic methods, the results obtained are more scientific and reasonable
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Dong, Guofang, Pu Sun, Wenbo Shi, and Chang Choi. "A novel valuation pruning optimization fuzzing test model based on mutation tree for industrial control systems." Applied Soft Computing 70 (September 2018): 896–902. http://dx.doi.org/10.1016/j.asoc.2018.02.036.
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Dai, Xinghua, Shengrong Gong, Shan Zhong, and Zongming Bao. "Bilinear CNN Model for Fine-Grained Classification Based on Subcategory-Similarity Measurement." Applied Sciences 9, no. 2 (January 16, 2019): 301. http://dx.doi.org/10.3390/app9020301.
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One of the challenges in fine-grained classification is that subcategories with significant similarity are hard to be distinguished due to the equal treatment of all subcategories in existing algorithms. In order to solve this problem, a fine-grained image classification method by combining a bilinear convolutional neural network (B-CNN) and the measurement of subcategory similarities is proposed. Firstly, an improved weakly supervised localization method is designed to obtain the bounding box of the main object, which allows the model to eliminate the influence of background noise and obtain more accurate features. Then, sample features in the training set are computed by B-CNN so that the fuzzing similarity matrix for measuring interclass similarities can be obtained. To further improve classification accuracy, the loss function is designed by weighting triplet loss and softmax loss. Extensive experiments implemented on two benchmarks datasets, Stanford Cars-196 and Caltech-UCSD Birds-200-2011 (CUB-200-2011), show that the newly proposed method outperforms in accuracy several state-of-the-art weakly supervised classification models.
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Wang, Xiandong, Jianmin He, and Shouwei Li. "Compound Option Pricing under Fuzzy Environment." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/875319.
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Considering the uncertainty of a financial market includes two aspects: risk and vagueness; in this paper, fuzzy sets theory is applied to model the imprecise input parameters (interest rate and volatility). We present the fuzzy price of compound option by fuzzing the interest and volatility in Geske’s compound option pricing formula. For eachα, theα-level set of fuzzy prices is obtained according to the fuzzy arithmetics and the definition of fuzzy-valued function. We apply a defuzzification method based on crisp possibilistic mean values of the fuzzy interest rate and fuzzy volatility to obtain the crisp possibilistic mean value of compound option price. Finally, we present a numerical analysis to illustrate the compound option pricing under fuzzy environment.
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Liu, Xiao, Xiaoting Li, Rupesh Prajapati, and Dinghao Wu. "DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1044–51. http://dx.doi.org/10.1609/aaai.v33i01.33011044.
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Compilers are among the most fundamental programming tools for building software. However, production compilers remain buggy. Fuzz testing is often leveraged with newlygenerated, or mutated inputs in order to find new bugs or security vulnerabilities. In this paper, we propose a grammarbased fuzzing tool called DEEPFUZZ. Based on a generative Sequence-to-Sequence model, DEEPFUZZ automatically and continuously generates well-formed C programs. We use this set of new C programs to fuzz off-the-shelf C compilers, e.g., GCC and Clang/LLVM. We present a detailed case study to analyze the success rate and coverage improvement of the generated C programs for fuzz testing. We analyze the performance of DEEPFUZZ with three types of sampling methods as well as three types of generation strategies. Consequently, DEEPFUZZ improved the testing efficacy in regards to the line, function, and branch coverage. In our preliminary study, we found and reported 8 bugs of GCC, all of which are actively being addressed by developers.
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Shriver, David, Sebastian Elbaum, Matthew B. Dwyer, and David S. Rosenblum. "Evaluating Recommender System Stability with Influence-Guided Fuzzing." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4934–42. http://dx.doi.org/10.1609/aaai.v33i01.33014934.
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Recommender systems help users to find products or services they may like when lacking personal experience or facing an overwhelming set of choices. Since unstable recommendations can lead to distrust, loss of profits, and a poor user experience, it is important to test recommender system stability. In this work, we present an approach based on inferred models of influence that underlie recommender systems to guide the generation of dataset modifications to assess a recommender’s stability. We implement our approach and evaluate it on several recommender algorithms using the MovieLens dataset. We find that influence-guided fuzzing can effectively find small sets of modifications that cause significantly more instability than random approaches.
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Wang, Hong, Yu Qiu Liu, and Li Hui Zhou. "Research on an Improved Cellular Automata Model." Applied Mechanics and Materials 160 (March 2012): 109–14. http://dx.doi.org/10.4028/www.scientific.net/amm.160.109.
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We introduce type-2 fuzzy reasoning to models of cellular automata, and combine type-2 fuzzy logic and classic cellular automata model to establish a new model of evolution reasoning, cellular automata model based on type-2 fuzzy logic. The key parts of cellular automata are transition functions and cell states. We fuzzify the transition functions of cellular automata into type-2 fuzzy rules, and cell states into type-2 fuzzy states too. Thus, we establish an improved cellular automata model based on type-2 fuzzy logic.
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Idowu, Peter Adebayo, Sarumi Olusegun Ajibola, Jeremiah Ademola Balogun, and Oluwadare Ogunlade. "Development of a Fuzzy Logic-based Model for Monitoring Cardiovascular Risk." International Journal of Healthcare Information Systems and Informatics 10, no. 4 (October 2015): 38–55. http://dx.doi.org/10.4018/ijhisi.2015100103.
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Cardiovascular diseases (CVD) are top killers with heart failure as one of the most leading cause of death in both developed and developing countries. In Nigeria, the inability to consistently monitor the vital signs of patients has led to the hospitalization and untimely death of many as a result of heart failure. Fuzzy logic models have found relevance in healthcare services due to their ability to measure vagueness associated with uncertainty management in intelligent systems. This study aims to develop a fuzzy logic model for monitoring heart failure risk using risk indicators assessed from patients. Following interview with expert cardiologists, the different stages of heart failure was identified alongside their respective indicators. Triangular membership functions were used to fuzzify the input and output variables while the fuzzy inference engine was developed using rules elicited from cardiologists. The model was simulated using the MATLAB® Fuzzy Logic Toolbox.
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Bo, Rui Feng. "Concept Optimization Model with Multilevel Hierarchy Based on Fuzzy Neural Network." Advanced Materials Research 338 (September 2011): 30–33. http://dx.doi.org/10.4028/www.scientific.net/amr.338.30.
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To implement optimization for mechanical concepts acquired by function analysis more effectively, BP neural network is adopted to structure multilevel evaluation model, which capitalizes on the features of nonlinearity, self-organization, and fault tolerance of neural network. By using appropriate data sets to train the neural network, expertise is acquired and expressed using a trained weight and threshold matrix. Once evaluation objectives of each candidate are fuzzily quantified, converted into evaluation attribute value, and fed into the trained network model, the optimal concept can be obtained. During the process, neural network is used to solve the bottle-neck problem of knowledge acquisition and expression and can be viewed as knowledge base and reasoning engine for the optimization. Hence the proposed evaluation model can effectively deal with concept evaluation and optimization problem with multilevel objective system.
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Pierce, Allan D. "Fuzzier but simpler analytic models for physical acoustics and structural acoustics." Journal of the Acoustical Society of America 103, no. 5 (May 1998): 2954. http://dx.doi.org/10.1121/1.422325.
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Dhaundiyal, Alok, and Suraj B. Singh. "Analysis of the non Isothermal Distributed Activation Energy Model for Biomass Pyrolysis by Fuzzy Gaussian Distribution." Rural Sustainability Research 35, no. 330 (June 1, 2016): 32–41. http://dx.doi.org/10.1515/plua-2016-0005.
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Abstract The main aim of this paper is to fuzzify the kinetic parameters, which have crisp nature, in order to obtain the realistic and accurate results. In the present study, the variance, upper limit of ‘dE’ and the frequency factor are assumed to be fuzzy numbers. The Gaussian distribution is considered as the distribution function, f (E), of Distributed Activation Energy Model (DAEM). The membership and the non-membership functions are evaluated by the trapezoidal fuzzy number. Thermo-analytical data has been found experimentally with the help of TGA/DTG analysis. The approximated solution of DAEM is obtained with the help of asymptotic expansion.
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Wu, Linlin, Ruiying Zhao, Yuyu Li, and Ye-Hwa Chen. "Optimal Design of Adaptive Robust Control for the Delta Robot with Uncertainty: Fuzzy Set-Based Approach." Applied Sciences 10, no. 10 (May 18, 2020): 3472. http://dx.doi.org/10.3390/app10103472.
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An optimal control design for the uncertain Delta robot is proposed in the paper. The uncertain factors of the Delta robot include the unknown dynamic parameters, the residual vibration disturbances and the nonlinear joints friction, which are (possibly fast) time-varying and bounded. A fuzzy set theoretic approach is creatively used to describe the system uncertainty. With the fuzzily depicted uncertainty, an adaptive robust control, based on the fuzzy dynamic model, is established. It designs an adaptation mechanism, consisting of the leakage term and the dead-zone, to estimate the uncertainty information. An optimal design is constructed for the Delta robot and solved by minimizing a fuzzy set-based performance index. Unlike the traditional fuzzy control methods (if-then rules-based), the proposed control scheme is deterministic and fuzzily optimized. It is proven that the global solution in the closed form for this optimal design always exists and is unique. This research provides the Delta parallel robot a novel optimal control to guarantee the system performance regardless of the uncertainty. The effectiveness of the proposed control is illustrated by a series of simulation experiments. The results reveal that the further applications in other robots are feasible.
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Yabuuchi, Yoshiyuki. "Fuzzification Methods and Prediction Accuracy of Fuzzy Autocorrelation Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 6 (October 20, 2017): 1009–16. http://dx.doi.org/10.20965/jaciii.2017.p1009.
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The fuzzy autocorrelation model is a fuzzified autoregressive (AR) model. The aim of the fuzzy autocorrelation model is to describe the possible states of the system with high accuracy. This model uses autocorrelation similar to the Box–Jenkins model. The fuzzy autocorrelation model occasionally increases the vagueness. Although the problem can be mitigated using fuzzy confidence intervals instead of fuzzy time-series data, the unnatural estimations do not improve. Subsequently, an alternate method was used to fuzzify the time-series data and mitigate the unnatural estimation problem. This method also improved the model prediction accuracy. This paper focuses on fuzzification method, and discusses the prediction accuracy of the model and fuzzification of the time-series data. The analysis of the Nikkei stock average shows a high prediction accuracy and manageability of a fuzzy autocorrelation model. In this pape, a quartile is employed as an alternate fuzzification method. The model prediction accuracy and estimation behavior are verified through an analysis. Finally, the proposed method was found to be successful in mitigating the problems.
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Zahlevi, Alifah, Alan Prahutama, and Abdul Hoyyi. "PEMODELAN KECEPATAN ANGIN DI KOTA SEMARANG MENGGUNAKAN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)." Jurnal Gaussian 8, no. 3 (August 30, 2019): 296–304. http://dx.doi.org/10.14710/j.gauss.v8i3.26709.
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Semarang city is the one of the strategic areas located in the middle of the north coast of Java that has a tropical climate with the high humidity and temperature, so it often causes a high rainfall and strong wind. So that is way Semarang city is ever sustained the extreme weather like a Tropical Storm. Since January 2016 until 2017 there are 34 cases of Tornado and 24 incidents of fallen trees because of the gale. For helping the people to be allert the effect of the strong winds can be done by predicting the average of wind velocity by using Adaptive Neuro-Fuzzy Inference System (ANFIS) method which can predict the climate change that do not require the assumption of white noise and normal residual distribution. In addition ANFIS is a group of neural network with input that has been fuzzied on the first or second layer, but the weight of the artificial neural is not fuzzied. The identification result of stationaries obtained the plot of PACF on the first and second lag, with the result that these lag which will be a input variable on ANFIS model. The result of ANFIS by using cluster FCM, the third total membership show the smallest percentage of RMSE in-sample is 0,0048 on the first lag, and the smallest percentage of RMSE out-sample is 0,008 on the ANFIS model with the input lag 1 and three cluster. Keywords : the average of wind velocity, ANFIS, RMSE
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Tsai, Ying Chieh, Ching Hsue Cheng, and Jing Rong Chang. "A New Knowledge Discovery Model for Extracting Diagnosis Rules of Manufacturing Process." Materials Science Forum 505-507 (January 2006): 889–94. http://dx.doi.org/10.4028/www.scientific.net/msf.505-507.889.
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The knowledge obtained from the experience of monitoring manufacturing process is critical to guarantee good products produced at the end of manufacturing line. Recently, many methods have been developed for the described purpose above. In this paper, a new knowledge discovery model based on soft computing is proposed. The proposed model contains a new algorithm Modified Correlation-based Feature Selection (MCFS), a new algorithm Modified Minimum Entropy Principle Algorithm (MMEPA), and Variable Precision Rough Set Model (VP-model). After conducting a real case of monitoring the process of manufacturing industrial conveyor belt, some advantages of the proposed model are that (1) MCFS can quickly identifying and screening irrelevant, redundant, and noisy features for data reduction; (2) MMEPA can objectively construct membership functions of fuzzy sets for fuzzifing the reduced dataset; (3) VP-model can extract causal relationship rules for controlling product quality; (4) Extracted rules by the proposed knowledge discovery model are easily understood and interpretable.
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Rios, Anthony. "FuzzE: Fuzzy Fairness Evaluation of Offensive Language Classifiers on African-American English." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 881–89. http://dx.doi.org/10.1609/aaai.v34i01.5434.
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Hate speech and offensive language are rampant on social media. Machine learning has provided a way to moderate foul language at scale. However, much of the current research focuses on overall performance. Models may perform poorly on text written in a minority dialectal language. For instance, a hate speech classifier may produce more false positives on tweets written in African-American Vernacular English (AAVE). To measure these problems, we need text written in both AAVE and Standard American English (SAE). Unfortunately, it is challenging to curate data for all linguistic styles in a timely manner—especially when we are constrained to specific problems, social media platforms, or by limited resources. In this paper, we answer the question, “How can we evaluate the performance of classifiers across minority dialectal languages when they are not present within a particular dataset?” Specifically, we propose an automated fairness fuzzing tool called FuzzE to quantify the fairness of text classifiers applied to AAVE text using a dataset that only contains text written in SAE. Overall, we find that the fairness estimates returned by our technique moderately correlates with the use of real ground-truth AAVE text. Warning: Offensive language is displayed in this manuscript.
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Peng, Wei, and Rene V. Mayorga. "A Fuzzy-stochastic Approach for Binary Linear Programming under Uncertainties." Journal of Mathematics Research 9, no. 3 (May 30, 2017): 95. http://dx.doi.org/10.5539/jmr.v9n3p95.
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This paper presents an innovative Fuzzy-Stochastic Approach (FSA) to solve Binary Linear Programming (BLP) problems under uncertainties. An Interval-coefficient Fuzzy Binary Linear Programming (IFBLP) model is applied here to reflect two different types of uncertainty in a BLP problem. In the proposed IFBLP model the interval coefficient is used to reflect parameter uncertainty, and the fuzzy goal & fuzzy constraints are used to represent model structure uncertainty. The proposed FSA would de-fuzzify the fuzzy constraints in an IFBLP model by considering its fuzzy goal; and then derive two linear BLPs with extreme crisp-coefficients from the IFBLP model, which here are called as a best optimum BLP and a worst optimum BLP. The results of the two-extreme linear BLPs are used to bound the outcome distribution of the IFBLP model. The proposed FSA is applied into a long-term traffic noise control planning to present its applicability.
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Wu, Jian De. "A New Evaluation Approach to Mechanical Kinematic Concept Using Fuzzy Neural Network." Advanced Materials Research 421 (December 2011): 666–69. http://dx.doi.org/10.4028/www.scientific.net/amr.421.666.
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In this paper, BP network is applied to structure multi-level evaluation model to implement evaluation for the kinematic concepts acquired by function analysis. Under this approach, the best concept can be selected once evaluation indicators of each candidate are fuzzily quantified, converted into evaluation attribute value, and fed into the trained network model. During the process, neural network is used to solve the bottle-neck problem of knowledge acquiring and expression, which can be viewed as knowledge base and reasoning engine for the evaluation. At the same time, it is effective in solving the problem of weight distribution in evaluation indicator system. Fuzzy logic is used to achieve the fuzzy quantization for the attribute value of evaluation indicator in evaluation system, which can be used as the I/O value for neural network.
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Zheng, Bao Guo, Bing Xu, and Zhong Jin Shi. "The Researches and Applications Based on Video Statistical Analysis of Average Population Density Estimation Methods." Applied Mechanics and Materials 513-517 (February 2014): 4539–42. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4539.
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This article describes the use of the average population density estimation methods based on video statistical analysis, and mainly discussed the research and application of the air conditioning energy-efficient system in the subway. The distributed intelligent control system in the subway station platform captured video images by more than one camera sensors, according to the computer image processing methods. And it have unique advantages for the fuzzy neural network to model the human nervous system in fuzzy information processing. When tackling the video files, the image boundary is fuzzily set, it can legitimately divide the crowd by achieving the image intelligent analysis data, and whats more, it can help to get the estimation of population density.
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Hu, Xintong, Lingling Yao, Yujing Zhang, Zhuo Meng, and Yize Sun. "Optimizing structural parameters of carbon fiber braiding carriers based on antlion optimization algorithm." Journal of Industrial Textiles 50, no. 4 (February 27, 2019): 460–82. http://dx.doi.org/10.1177/1528083719831085.
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Braiding carriers, which are the important parts of a braiding machine, have the functions such as carrying braiding materials, controlling tension of carbon fiber, and driving carbon fiber movement. During the braiding process, two groups of carbon fibers braided in clockwise and counter clockwise direction contact each other and form relative motion, which causes friction and fuzzing. In order to improve this situation, the structural parameters of the carriers need to be optimized. In this paper, the kinematics and dynamics models were established based on the structure of braiding carriers. The micro-element method was used to analyze the relationship between the fiber length released from the yarn barrel, the rotation angle of the lever, and the tension of the carbon fiber. To limit the fluctuant range of carbon fiber tension, and to alleviate the fluffing phenomenon caused by the two groups of carbon fiber in contact with each other, antlion algorithm was used to optimize the structural parameters of braiding carriers. The simulation results showed that the tension of the carbon fiber can meet the processing requirements by adjusting the starting angle of each stage of carrier, the length of lever, the elastic coefficient of springs, and pre-compression of springs. It can be known that the structural parameters of braiding carriers optimized by antlion algorithm could meet the requirement of carbon fiber tension.
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Jaggi, Chandra, Sarla Pareek, Aditi Khanna, and N. Nidhi. "Optimal replenishment policy for fuzzy inventory model with deteriorating items and allowable shortages under inflationary conditions." Yugoslav Journal of Operations Research 26, no. 4 (2016): 507–26. http://dx.doi.org/10.2298/yjor150202002y.
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This study develops an inventory model to determine ordering policy for deteriorating items with constant demand rate under inflationary condition over a fixed planning horizon. Shortages are allowed and are partially backlogged. In today?s wobbling economy, especially for long term investment, the effects of inflation cannot be disregarded as uncertainty about future inflation may influence the ordering policy. Therefore, in this paper a fuzzy model is developed that fuzzify the inflation rate, discount rate, deterioration rate, and backlogging parameter by using triangular fuzzy numbers to represent the uncertainty. For Defuzzification, the well known signed distance method is employed to find the total profit over the planning horizon. The objective of the study is to derive the optimal number of cycles and their optimal length so to maximize the net present value of the total profit over a fixed planning horizon. The necessary and sufficient conditions for an optimal solution are characterized. An algorithm is proposed to find the optimal solution. Finally, the proposed model has been validated with numerical example. Sensitivity analysis has been performed to study the impact of various parameters on the optimal solution, and some important managerial implications are presented.
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Tinh, Nghiem Van, and Nguyen Cong Dieu. "IMPROVING THE FORECASTED ACCURACY OF MODEL BASED ON FUZZY TIME SERIES AND K-MEANS CLUSTERING." Journal of Science and Technology: Issue on Information and Communications Technology 3, no. 2 (December 31, 2017): 46. http://dx.doi.org/10.31130/jst.2017.47.
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There are many approaches to improve the forecasted accuracy of model based on fuzzy time series such as: determining the optimal interval length, establishing fuzzy logic relationship groups, similarity measures, …wherein, the length of intervals is a factor that greatly affects forecasting results in fuzzy time series model. In this paper, a new forecasting model based on combining the fuzzy time series (FTS) and K-mean clustering algorithm with three computational methods, K-means clustering technique, the time - variant fuzzy logical relationship groups and defuzzification forecasting rules, is presented. Firstly, we apply the K-mean clustering algorithm to divide the historical data into clusters and tune them into intervals with proper lengths. Then, based on the new intervals obtained, the proposed method is used to fuzzify all the historical data and create the time -variant fuzzy logical relationship groups based on the new concept of time – variant fuzzy logical relationship group. Finally, Calculate the forecasted output value by the improved defuzzification technique in the stage of defuzzification. To evaluate performance of the proposed model, two numerical data sets are utilized to illustrate the proposed method and compare the forecasting accuracy with existing methods. The results show that the proposed model gets a higher average forecasting accuracy rate to forecast the Taiwan futures exchange (TAIFEX) and enrollments of the University of Alabama than the existing methods based on the first – order and high-order fuzzy time series.
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Biswas, Animesh, and Debasish Majumder. "Genetic Algorithm Based Hybrid Fuzzy System for Assessing Morningness." Advances in Fuzzy Systems 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/732831.
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This paper describes a real life case example on the assessment process of morningness of individuals using genetic algorithm based hybrid fuzzy system. It is observed that physical and mental performance of human beings in different time slots of a day are majorly influenced by morningness orientation of those individuals. To measure the morningness of people various self-reported questionnaires were developed by different researchers in the past. Among them reduced version of Morningness-Eveningness Questionnaire is mostly accepted. Almost all of the linguistic terms used in questionnaires are fuzzily defined. So, assessing them in crisp environments with their responses does not seem to be justifiable. Fuzzy approach based research works for assessing morningness of people are very few in the literature. In this paper, genetic algorithm is used to tune the parameters of a Mamdani fuzzy inference model to minimize error with their predicted outputs for assessing morningness of people.
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Qin, Jian, and Shi Qun Yin. "Research of Chinese Multi-Pattern Fuzzy Matching Method for SMS Spam Monitoring." Advanced Materials Research 532-533 (June 2012): 748–52. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.748.
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In this paper, it is analyzed the content and achieving mechanism of WM algorithm on account of frequently asked questions during the monitoring and filtering of Short Message Service(SMS) spam, and brought forward a kind of Chinese message with multi- patterns fuzzy matching method to improve WM algorithm to apply to Chinese spam message filtering. The method firstly does pretreatment of key words and fuzzification of monitor for message on account of expressive diversity of Chinese message, and then realizes initial matching by improving WM algorithm. Because the preliminary result is not the final one, it needs further fuzzily match for the result. It does particular introduction of the method and the model of monitor and filtration for Chinese SMS spam in the paper. The method’s correctness and performing efficiency also has been done experimental analysis, test and comparison experiment with former system that had not been done pretreatment. Experimental results show the validity of the improving algorithms. The method is testing further during the practice.
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Aggarwal, Anil K. "Using Deming's Cycle for Improvement in a Course." International Journal of Web-Based Learning and Teaching Technologies 15, no. 3 (July 2020): 31–45. http://dx.doi.org/10.4018/ijwltt.2020070103.
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The boundaries between accounting and technology is becoming fuzzier as accounting companies are becoming consulting companies. Digital economies are changing business models and companies that do not adept can become obsolete very fast. Even professional organizations are recommending using technology to modernize, automate and expedite accounting discipline. Therefore, it is necessary to train personnel to become competent in both technology and accounting. Universities are fulfilling this requirement by offering courses such as Accounting Information Systems, data analytics, big data, etc. This article uses Deming's PlanDoCheckAct (PDCA) cycle for longitudinal assessment and improvement of the AIS course. Instead of re-inventing the wheel, instructors can learn from our experience. This article would be useful for instructors trying different and emerging approaches. In addition, this article would be useful for instructors trying to engage students and to train them for future challenges.
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Meng, Shuo, Zhenhui Du, Liming Yuan, Shuanke Wang, Ruiyan Han, and Xiaoyu Wang. "Membership Function-Weighted Non-Linear Fitting Method for Optical-Sensing Modeling and Reconstruction." Sensors 18, no. 11 (November 3, 2018): 3762. http://dx.doi.org/10.3390/s18113762.
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Imprecise measurements present universally due to variability in the measurement error. We devised a very simple membership function to evaluate fuzzily the quality of optical sensing with a small dataset, where a normal distribution cannot be assumed. The proposed membership function was further used as a weighting function for non-linear curve fitting under expected mathematical model constraints, namely the membership function-weighted Levenberg–Marquardt (MFW-LM) algorithm. The robustness and effectiveness of the MFW-LM algorithm were demonstrated by an optical-sensing simulation and two practical applications. (1) In laser-absorption spectroscopy, molecular spectral line modeling was greatly improved by the method. The measurement uncertainty of temperature and pressure were reduced dramatically, by 53.3% and 43.5%, respectively, compared with the original method. (2) In imaging, a laser beam-profile reconstruction from heavy distorted observations was improved by the method. As the dynamic range of the infrared camera increased from 256 to 415, the detailed resolution of the laser-beam profiles increased by an amazing 360%, achieving high dynamic-range imaging to capture optical signal details. Therefore, the MFW-LM algorithm provides a robust and effective tool for fitting a proper physical model and precision parameters from low-quality data.
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Tang, Wei-Ling, Jinn-Tsong Tsai, and Yao-Mei Chen. "Fuzzy logic and Gagné learning hierarchy for assessing mathematics skills." Science Progress 104, no. 2 (April 2021): 003685042110143. http://dx.doi.org/10.1177/00368504211014346.
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This study developed a fuzzy logic and Gagné learning hierarchy (FL-GLH) for assessing mathematics skills and identifying learning barrier points. Fuzzy logic was used to model the human reasoning process in linguistic terms. Specifically, fuzzy logic was used to build relationships between skill level concepts as inputs and learning achievement as an output. Gagné learning hierarchy was used to develop a learning hierarchy diagram, which included learning paths and test questions for assessing mathematics skills. First, the Gagné learning hierarchy was used to generate learning path diagrams and test questions. In the second step, skill level concepts were grouped, and their membership functions were established to fuzzify the input parameters and to build membership functions of learning achievement as an output. Third, the inference engine generated fuzzy values by applying fuzzy rules based on fuzzy reasoning. Finally, the defuzzifier converted fuzzy values to crisp output values for learning achievement. Practical applications of the FL-GLH confirmed its effectiveness for evaluating student learning achievement, for finding student learning barrier points, and for providing teachers with guidelines for improving learning efficiency in students.
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Mohammed Abbas, Amthal, and Sarab Kadir Mugair. "A Morpho-Syntactic Analysis of Modal Verbs in Iraqi Dialects: A Comparative Study." International Journal of Applied Linguistics and English Literature 7, no. 1 (December 15, 2017): 107. http://dx.doi.org/10.7575/aiac.ijalel.v.7n.1p.107.
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This study is an attempt to investigate and analyze the linguistic forms of modal verbs in three Iraqi dialects. These modal verbs are "can", "will" and "must” and the dialects under study are Musiliyah that is spoken in Mosul province in the north of Iraq, Baghdadiyah that is spoken in Baghdad city in the middle of Iraq and Basriyah that is spoken in the city of Basrah in the south. The study adopts the descriptive and comparative techniques of James' theory (1998) to analyze the data. The present study aims to investigate and analyze three modal verbs in three Iraqi dialects. It tends to share the same views of Ma'ruf (2011:24) and James (1998:19) who state that nations’ dialects can be fruitful which in turn can enrich their mother tongue, in disagreement with Lakoff (1972:) who argues that dialects may hinder their mother tongue which eventually can be fuzzier than of great help. The findings of the present study reveal that, in a way or another, speakers of IDs can use different kinds of clauses to convey the general and predicative meaning simultaneously. Dialects can help speakers of IDs reinforce their specificities and preferences, i.e., dialects strengthen their ability to create new words, which at long last, enrich their mother tongue. The analysis of the present study proves the usability of Ma'ruf’s (2011) and James’ (1998) standing regarding the dialects. Contrarily, the analysis also shows that Lakoff's views (1972) are not , to an extent, objective, i.e., her views are relative.
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Ma, Ru Hui, and Yuan Liu. "Wavelet Fuzzy Neural Network Based on Modified QPSO for Network Anomaly Detection." Applied Mechanics and Materials 20-23 (January 2010): 1378–84. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.1378.
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Neural network (NN) employed to settle network anomaly has become prevalent. However, traditional training algorithm for NN is not optimum, that is, often suboptimum, and encountering complicated network anomaly, an adaptive yet efficient NN or hybrid NN model should be better considered. Therefore, this paper proposes a novel network anomaly detection method employing wavelet fuzzy neural network (WFNN) to use modified Quantum-Behaved Particle Swarm Optimization (QPSO). In this paper, wavelet transform is applied to extract fault characteristics from the anomaly state. Fuzzy theory and neural network are employed to fuzzify the extracted information. Wavelet is then integrated with fuzzy neural network to form the wavelet fuzzy neural network (WFNN). The Quantum-Behaved Particle Swarm Optimization, which outperforms other optimization algorithm considerably on its simple architecture and fast convergence, has previously applied to solve optimum problem. However, the QPSO also has its own shortcomings. So, there exists a modified QPSO which is used to train WFNN in this paper. Experimental result on KDD99 intrusion detection datasets shows that this WFNN using the novel training algorithm has high detection rate while maintaining a low false positive rate.
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Sheng, Siqing, Pengwang Li, Hao Wu, Liwei Zhang, and Maosen Fan. "Research on Variable Inertia Coordination Frequency Regulation Strategy Based on a Wind & Thermal Power Combined System." E3S Web of Conferences 118 (2019): 02042. http://dx.doi.org/10.1051/e3sconf/201911802042.
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With the increasing installed capacity of the wind power, the power system has an obviously low inertia characteristic. It is of great significance to actively promote the virtual inertia frequency regulation technology of wind turbines (WTS) for improving the system frequency quality. The frequency regulation capability and frequency regulation effects of wind & thermal power units were analysed, and a variable inertia coordination frequency regulation strategy for different wind power penetration conditions was proposed in this paper. At the wind farm level, the dynamic frequency regulation participation coefficient of wind farms was fuzzily determined according to the operation conditions of WTS and the wind power penetration ratio. At the wind turbine level, the calculation method of the equivalent inertia constant of WTS was given based on the effective rotational kinetic energy. And the allowable range of frequency regulation parameters of WTS was determined by considering the incremental model of the system. Results indicated that the proposed coordinated frequency regulation strategy not only provided a reliable inertia support, but also maintained the stability of WTS. The frequency response performance of the high-penetration wind power system was improved.
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AUSSEM, ALEX, FIONN MURTAGH, and MARC SARAZIN. "DYNAMICAL RECURRENT NEURAL NETWORKS — TOWARDS ENVIRONMENTAL TIME SERIES PREDICTION." International Journal of Neural Systems 06, no. 02 (June 1995): 145–70. http://dx.doi.org/10.1142/s0129065795000123.
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Dynamical Recurrent Neural Networks (DRNN) (Aussem 1995a) are a class of fully recurrent networks obtained by modeling synapses as autoregressive filters. By virtue of their internal dynamic, these networks approximate the underlying law governing the time series by a system of nonlinear difference equations of internal variables. They therefore provide history-sensitive forecasts without having to be explicitly fed with external memory. The model is trained by a local and recursive error propagation algorithm called temporal-recurrent-backpropagation. The efficiency of the procedure benefits from the exponential decay of the gradient terms backpropagated through the adjoint network. We assess the predictive ability of the DRNN model with meteorological and astronomical time series recorded around the candidate observation sites for the future VLT telescope. The hope is that reliable environmental forecasts provided with the model will allow the modern telescopes to be preset, a few hours in advance, in the most suited instrumental mode. In this perspective, the model is first appraised on precipitation measurements with traditional nonlinear AR and ARMA techniques using feedforward networks. Then we tackle a complex problem, namely the prediction of astronomical seeing, known to be a very erratic time series. A fuzzy coding approach is used to reduce the complexity of the underlying laws governing the seeing. Then, a fuzzy correspondence analysis is carried out to explore the internal relationships in the data. Based on a carefully selected set of meteorological variables at the same time-point, a nonlinear multiple regression, termed nowcasting (Murtagh et al. 1993, 1995), is carried out on the fuzzily coded seeing records. The DRNN is shown to outperform the fuzzy k-nearest neighbors method.
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Abhishekh, Surendra Singh Gautam, and S. R. Singh. "A Score Function-Based Method of Forecasting Using Intuitionistic Fuzzy Time Series." New Mathematics and Natural Computation 14, no. 01 (March 2018): 91–111. http://dx.doi.org/10.1142/s1793005718500072.
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Intuitionistic fuzzy set plays a vital role in data analysis and decision-making problems. In this paper, we propose an enhanced and versatile method of forecasting using the concept of intuitionistic fuzzy time series (FTS) based on their score function. The developed method has been presented in the form of simple computational steps of forecasting instead of complicated max–min compositions operator of intuitionistic fuzzy sets to compute the relational matrix [Formula: see text]. Also, the proposed method is based on the maximum score and minimum accuracy function of intuitionistic fuzzy numbers (IFNs) to fuzzify the historical time series data. Further intuitionistic fuzzy logical relationship groups are defined and also provide a forecasted value and lies in an interval and is more appropriate rather than a crisp value. Furthermore, the proposed method has been implemented on the historical student enrollments data of University of Alabama and obtains the forecasted values which have been compared with the existing methods to show its superiority. The suitability of the proposed model has also been examined to forecast the movement of share market price of State Bank of India (SBI) at Bombay Stock Exchange (BSE). The results of the comparison of MSE and MAPE indicate that the proposed method produces more accurate forecasting results.
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Xia, Hui, San-shun Zhang, Ben-xia Li, Li Li, and Xiang-guo Cheng. "Towards a Novel Trust-Based Multicast Routing for VANETs." Security and Communication Networks 2018 (October 1, 2018): 1–12. http://dx.doi.org/10.1155/2018/7608198.
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The Intelligent Transportation System (ITS) is an important application area of the Cyber-Physical System (CPS). To further promote effective communication between vehicles, vehicular ad hoc networks (VANETs) have been widely used in the ITS. However, the communication efficiency in VANETs is not only affected by the external environment but also more vulnerable to malicious attacks. In order to address the above-mentioned issues, we propose a novel trust-based multicast routing protocol (TMR) to defend against multiple attacks and improve the routing efficiency. In the proposed trust model, direct trust is calculated based on Bayesian theory and indirect trust is computed according to evaluation credibility and activity. The fuzzy logic theory is used to fuzzify the direct and indirect trust values, and then the total trust value of the node is obtained by defuzzification. With the help of the obtained trust values, malicious vehicle nodes are eliminated in the processes of route establishment and route maintenance, and finally, the network establishes trusted and efficient routes for data delivery. Comprehensive simulation experiments show that our new protocol can effectively improve the transmission rate of data packets at the expense of a slight increase in end-to-end delay and control overhead.
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De, Sujit Kumar, and Shib Sankar Sana. "Two-layer supply chain model for Cauchy-type stochastic demand under fuzzy environment." International Journal of Intelligent Computing and Cybernetics 11, no. 2 (June 11, 2018): 285–308. http://dx.doi.org/10.1108/ijicc-10-2016-0037.
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Purpose The purpose of this paper is to deal with profit maximization problem of two-layer supply chain (SC) under fuzzy stochastic demand having finite mean and unknown variance. Buyback policy is employed from the retailer to supplier. The profit of the supplier solely depends on the order size of the retailers. However, the loss of shortage items is related to loss of profit and goodwill dependent. The authors develop the profit function separately for both the retailer and supplier, first, for a decentralized system and, second, joining them, the authors get a centralized system (CS) of decision making, in which one is giving more profit to both of them. The problem is solved analytically first, then the authors fuzzify the model and solve by fuzzy Hausdorff distance method. Design/methodology/approach The analytical models are formed for both centralized and decentralized systems under non-cooperative and cooperative environment with suitable constraints. A significant assumption on density function, namely Cauchy-type density function, is introduced for demand rate because of its wider range of the retailers’ satisfactions. Fuzzy Hausdorff metric is incorporated within the fuzzy components of the fuzzy sets itself. Using this method, the authors find out closure values of both centralized and decentralized policies, which is an essential part of any cooperative and non-cooperative two-layer SC models. Moreover, the authors take care of the profit values with corresponding ambiguities for both the systems explicitly. Findings It is found that the centralize policy of SC could only be able to maximize the profit of both the retailers and suppliers. All analytical results are illustrated numerically along with sensitivity analysis and side by side comparative studies between Hausdorff and Euclidean distance measure are done exclusively. Research limitations/implications The main focus of attention of the proposed model is given to usefulness of Hausdorff distance. Unlike other distances, Hausdorff distance can take special care on the similarity measures of different fuzzy sets. Researchers have been engaged to use Hausdorff distance on the different fuzzy sets but, in this study, the authors have used it within the components of a same fuzzy set to gain more closure values than other methods. Originality/value The use of this Hausdorff distance approach is totally new as per literature survey suggested yet. However, the Cauchy-type density function has not been introduced anywhere in SC management problems by modern researchers still now. In crisp model, the sensitivity on goodwill measures really provides a special attention also.
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RAY, KUMAR S., and MANDRITA MONDAL. "CLASSIFICATION OF SODAR DATA BY DNA COMPUTING." New Mathematics and Natural Computation 07, no. 03 (September 2011): 413–32. http://dx.doi.org/10.1142/s1793005711002074.
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In this paper, we propose a wet lab algorithm for classification of SODAR data by DNA computing. The concept of DNA computing is essentially exploited to generate the classifier algorithm in the wet lab. The classifier is based on a new concept of similarity-based fuzzy reasoning suitable for wet lab implementation. This new concept of similarity-based fuzzy reasoning is different from conventional approach to fuzzy reasoning based on similarity measure and also replaces the logical aspect of classical fuzzy reasoning by DNA chemistry. Thus, we add a new dimension to the existing forms of fuzzy reasoning by bringing it down to nanoscale. We exploit the concept of massive parallelism of DNA computing by designing this new classifier in the wet lab. This newly designed classifier is very much generalized in nature and apart from SODAR data, this methodology can be applied to other types of data also. To achieve our goal we first fuzzify the given SODAR data in a form of synthetic DNA sequence which is called fuzzy DNA and which handles the vague concept of human reasoning. In the present approach, we can avoid the tedious choice of a suitable implication operator (for a particular operation) necessary for the classical approach to fuzzy reasoning based on fuzzy logic. We adopt the basic notion of DNA computing based on standard DNA operations. We consider double stranded DNA sequences, whereas, most of the existing models of DNA computation are based on single stranded DNA sequences. In the present model, we consider double stranded DNA sequences with a specific aim of measuring similarity between two DNA sequences. Such similarity measure is essential for designing the classifier in the wet lab. Note that, we have developed a completely new measure of similarity based on base pair difference which is absolutely different from the existing measure of similarity and which is very much suitable for expert system approach to classifier design, using DNA computing. In the present model of DNA computing, the end result of the wet lab algorithm produces multi valued status which can be linguistically interpreted to match the perception of an expert.
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Gadzhiev, Dzh N., E. G. Tagiev, N. Dzh Gadzhiev, and R. Yu Shikhlinskaya. "Application of fuzzy mathematical model of decision-making for the selection of optimal surgical tactics in patients with non-tumor obstructive jaundice." Kazan medical journal 99, no. 3 (June 15, 2018): 439–45. http://dx.doi.org/10.17816/kmj2018-439.
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Aim. Creation of a model of fuzzy logic for predicting the risk of postoperative complications and the choice of individual optimal surgical tactics in obstructive jaundice caused by choledocholithiasis. Methods. At the first stage, we determined the most prognostically significant factors affecting the risk of postoperative complications. In accordance with these factors, linguistic variables were introduced: X1 - patient’s age; X2 - duration of jaundice; X3 - temperature; X4 - comorbidities; X5 - the level of liver dysfunction; X6 - CD4+ in the blood; X7 - interleukin-2 in the serum; Y - level of risk. The intervals of their changes were determined. Fuzzi Logic Toolbox Matlab soft was used to achieve the determined aim. The values of input variables were introduced into the model, transformed in the «Phaser» block and then the rule base of the fuzzy inference system was formed by the expert method. As a result, the level of risk is determined and the choice of surgical tactics is made: (1) risk is absent or low (A); (2) doubtful risk (B) - if the risk assessment in the dynamics after preoperative therapy decreases, then tactics A, if the score does not decrease or increases, then tactics C; (3) high and very high risk (C) - an unequivocal choice of stage tactics. Results. According to the defined level of risk, in 92 patients a one-stage procedure was used, while 58 underwent a two-stage intervention. Due to the developed fuzzy mathematical model, forecasting of the optimal choice of surgical tactics is achieved, which significantly improves the results of treatment. Conclusion. The developed fuzzy mathematical model makes it possible to differentiate the choice of surgical tactics for a particular patient and thereby reduce the incidence of postoperative complications from 29.0 to 4.7% and mortality from 11.0 to 1.3%.
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Duarte, Henrique, and Diniz Lopes. "Career stages and occupations impacts on workers motivations." International Journal of Manpower 39, no. 5 (August 6, 2018): 746–63. http://dx.doi.org/10.1108/ijm-02-2017-0026.
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Purpose The career concept has become fuzzier due to changing work patterns, the ageing workforce and the environmental changes occurring during workers lifespans. Together this requires a renewed and broader reaching contextualization of this concept. The purpose of this paper is to set out an integrative approach arguing that the integration of career stage models with occupational groups proves more explanative of intrinsic and extrinsic worker motivations. Design/methodology/approach Secondary data from 23 European countries were drawn from the European Social Survey 2006. The construct validity and reliability of indicators was analyzed. Hypotheses were tested using discriminant analysis. Findings Results showed that neither occupations nor career stages are determinants per se of intrinsic motivations, but are better explained by their mutual integration. Career stages were shown to predict per se extrinsic motivations. Research limitations/implications The recourse to the European Social Survey pre-determined scales and the application of age ranges as proxies for careers stages suggested the usage of more specific measures in future studies. Practical implications Career management and compensation policies might be better tailored to worker motivations by considering the age ranges (as proxies of career stages) and workers’ occupations. Originality/value Findings evidenced the explanatory value of occupations for worker motivations and allowed putting into perspective the contextualization of not only boundaryless and protean career concepts, but also career stage theories. Data support the ecological validity of applying a career stages and occupations framework to a highly diversified and representative sample of European countries.
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Guo, Zhaoqiang, Shiran Liu, Jinping Liu, Yanhui Li, Lin Chen, Hongmin Lu, and Yuming Zhou. "How Far Have We Progressed in Identifying Self-admitted Technical Debts? A Comprehensive Empirical Study." ACM Transactions on Software Engineering and Methodology 30, no. 4 (July 2021): 1–56. http://dx.doi.org/10.1145/3447247.
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Background. Self-admitted technical debt (SATD) is a special kind of technical debt that is intentionally introduced and remarked by code comments. Those technical debts reduce the quality of software and increase the cost of subsequent software maintenance. Therefore, it is necessary to find out and resolve these debts in time. Recently, many automatic approaches have been proposed to identify SATD. Problem. Popular IDEs support a number of predefined task annotation tags for indicating SATD in comments, which have been used in many projects. However, such clear prior knowledge is neglected by existing SATD identification approaches when identifying SATD. Objective. We aim to investigate how far we have really progressed in the field of SATD identification by comparing existing approaches with a simple approach that leverages the predefined task tags to identify SATD. Method. We first propose a simple heuristic approach that fuzzily Matches task Annotation Tags ( MAT ) in comments to identify SATD. In nature, MAT is an unsupervised approach, which does not need any data to train a prediction model and has a good understandability. Then, we examine the real progress in SATD identification by comparing MAT against existing approaches. Result. The experimental results reveal that: (1) MAT has a similar or even superior performance for SATD identification compared with existing approaches, regardless of whether non-effort-aware or effort-aware evaluation indicators are considered; (2) the SATDs (or non-SATDs) correctly identified by existing approaches are highly overlapped with those identified by MAT ; and (3) supervised approaches misclassify many SATDs marked with task tags as non-SATDs, which can be easily corrected by their combinations with MAT . Conclusion. It appears that the problem of SATD identification has been (unintentionally) complicated by our community, i.e., the real progress in SATD comments identification is not being achieved as it might have been envisaged. We hence suggest that, when many task tags are used in the comments of a target project, future SATD identification studies should use MAT as an easy-to-implement baseline to demonstrate the usefulness of any newly proposed approach.
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Raskin, Lev, Oksana Sira, Larysa Sukhomlyn, and Yurii Parfeniuk. "Universal method for solving optimization problems under the conditions of uncertainty in the initial data." Eastern-European Journal of Enterprise Technologies 1, no. 4 (109) (February 26, 2021): 46–53. http://dx.doi.org/10.15587/1729-4061.2021.225515.
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This paper proposes a method to solve a mathematical programming problem under the conditions of uncertainty in the original data. The structural basis of the proposed method for solving optimization problems under the conditions of uncertainty is the function of criterion value distribution, which depends on the type of uncertainty and the values of the problem’s uncertain variables. In the case where independent variables are random values, this function then is the conventional theoretical-probabilistic density of the distribution of the random criterion value; if the variables are fuzzy numbers, it is then a membership function of the fuzzy criterion value. The proposed method, for the case where uncertainty is described in the terms of a fuzzy set theory, is implemented using the following two-step procedure. In the first stage, using the membership functions of the fuzzy values of criterion parameters, the values for these parameters are set to be equal to the modal, which are fitted in the analytical expression for the objective function. The resulting deterministic problem is solved. The second stage implies solving the problem by minimizing the comprehensive criterion, which is built as follows. By using an analytical expression for the objective function, as well as the membership function of the problem’s fuzzy parameters, applying the rules for operations over fuzzy numbers, one finds a membership function of the criterion’s fuzzy value. Next, one calculates a measure of the compactness of the resulting membership function of the fuzzy value of the problem’s objective function whose numerical value defines the first component of the integrated criterion. The second component is the rate of deviation of the desired solution to the problem from the previously received modal one. Absolutely similarly designed is the computational procedure for the case where uncertainty is described in the terms of a probability theory. Thus, the proposed method for solving optimization problems is universal in relation to the nature of the uncertainty in the original data. An important advantage of the proposed method is the ability to use it when solving any problem of mathematical programming under the conditions of fuzzily assigned original data, regardless of its nature, structure, and type
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Zhang, Yu, Wei Huo, Kunpeng Jian, Ji Shi, Longquan Liu, Yanyan Zou, Chao Zhang, and Baoxu Liu. "ESRFuzzer: an enhanced fuzzing framework for physical SOHO router devices to discover multi-Type vulnerabilities." Cybersecurity 4, no. 1 (July 19, 2021). http://dx.doi.org/10.1186/s42400-021-00091-9.
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AbstractSOHO (small office/home office) routers provide services for end devices to connect to the Internet, playing an important role in cyberspace. Unfortunately, security vulnerabilities pervasively exist in these routers, especially in the web server modules, greatly endangering end users. To discover these vulnerabilities, fuzzing web server modules of SOHO routers is the most popular solution. However, its effectiveness is limited due to the lack of input specification, lack of routers’ internal running states, and lack of testing environment recovery mechanisms. Moreover, existing works for device fuzzing are more likely to detect memory corruption vulnerabilities.In this paper, we propose a solution ESRFuzzer to address these issues. It is a fully automated fuzzing framework for testing physical SOHO devices. It continuously and effectively generates test cases by leveraging two input semantic models, i.e., KEY-VALUE data model and CONF-READ communication model, and automatically recovers the testing environment with power management. It also coordinates diversified mutation rules with multiple monitoring mechanisms to trigger multi-type vulnerabilities. With the guidance of the two semantic models, ESRFuzzer can work in two ways: general mode fuzzing and D-CONF mode fuzzing. General mode fuzzing can discover both issues which occur in the CONF and READ operation, while D-CONF mode fuzzing focus on the READ-op issues especially missed by general mode fuzzing.We ran ESRFuzzer on 10 popular routers across five vendors. In total, it discovered 136 unique issues, 120 of which have been confirmed as 0-day vulnerabilities we found. As an improvement of SRFuzzer, ESRFuzzer have discovered 35 previous undiscovered READ-op issues that belong to three vulnerability types, and 23 of them have been confirmed as 0-day vulnerabilities by vendors. The experimental results show that ESRFuzzer outperforms state-of-the-art solutions in terms of types and number of vulnerabilities found.
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Maggs, Kelly, and Vanessa Robins. "Topology-Inspired Method Recovers Obfuscated Term Information From Induced Software Call-Stacks." Frontiers in Applied Mathematics and Statistics 7 (May 28, 2021). http://dx.doi.org/10.3389/fams.2021.668082.
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Fuzzing is a systematic large-scale search for software vulnerabilities achieved by feeding a sequence of randomly mutated input files to the program of interest with the goal being to induce a crash. The information about inputs, software execution traces, and induced call stacks (crashes) can be used to pinpoint and fix errors in the code or exploited as a means to damage an adversary’s computer software. In black box fuzzing, the primary unit of information is the call stack: a list of nested function calls and line numbers that report what the code was executing at the time it crashed. The source code is not always available in practice, and in some situations even the function names are deliberately obfuscated (i.e., removed or given generic names). We define a topological object called the call-stack topology to capture the relationships between module names, function names and line numbers in a set of call stacks obtained via black-box fuzzing. In a proof-of-concept study, we show that structural properties of this object in combination with two elementary heuristics allow us to build a logistic regression model to predict the locations of distinct function names over a set of call stacks. We show that this model can extract function name locations with around 80% precision in data obtained from fuzzing studies of various linux programs. This has the potential to benefit software vulnerability experts by increasing their ability to read and compare call stacks more efficiently.
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Yao, Guoxiang, Quanlong Guan, and Kaibin Ni. "Test Model for Security Vulnerability in Web Controls based on Fuzzing." Journal of Software 7, no. 4 (April 27, 2012). http://dx.doi.org/10.4304/jsw.7.4.773-778.
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Sahadun, Nur Afiqah, Nor Azizah Ali, and Habibollah Haron. "PSO-FuzzyNN Techniques in Gender Classification Based on Bovine Bone Morphology Properties." International Journal of Innovative Computing 9, no. 1 (May 31, 2019). http://dx.doi.org/10.11113/ijic.v9n1.215.
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This simulation project aims to solve forensic anthropology issues by using the computational method. The positive identification on gender is such a potential field to be explored. Basically, gender identification in forensic anthropology by comparative skeletal anatomy by atlas and crucially affect the identification accuracy. The simulation identification method was studied in order to determine the best model, which reduce the total costs of the post-mortem as an objective. The computational method on simulation run improves the identification accuracy as proven by many studies. Fuzzy K-nearest neighbours classifier (FuzzyNN) is such a computational intelligence method and always shows the best performance in many fields including forensic anthropology. Thus, this intelligent identification method was implemented within the determining for best accuracy. The result of this proposed model was compared with raw data collection and standard collections datasets; Goldman Osteometric dataset and Ryan and Shaw Dataset (RSD) as a benchmark for the identification policy. To improve the accuracy of FuzzyNN classifier, Particle Swarm Optimization (PSO) feature selection was used as the basis for choosing the best features to be used by the selected FuzzyNN classification model. The model is called PSO-FuzzyNN and has been developed by MATLAB and WEKA tools platform. Comparisons of the performance measurement namely the percentage of the classification accuracy of the model were performed. The result show potential the proposed PSO-FuzzyNN method demonstrates the capability to the obtained highest accuracy of identification.
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"Inventory Model with Demand Dependent on Unit Price under Fuzzy Parameters and Decision Variables." International Journal of Recent Technology and Engineering 8, no. 3 (September 30, 2019): 784–88. http://dx.doi.org/10.35940/ijrte.c4013.098319.
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An EOQ model with demand dependent on unit price is considered and a new approach of finding optimal demand value is done from the optimal unit cost price after defuzzification. Here the cost parameters like setup cost, holding cost and shortage cost and also the decision variables like unit price, lot size and the maximum inventory are taken under fuzzy environment. Triangular fuzzy numbers are used to fuzzify these input parameters and unknown variables. For the proposed model an optimal solution has been determined using Karush Kuhn-Tucker conditions method. Graded Mean Integration (GMI) method is used for defuzzification. Numerical solutions are obtained and sensitivity analysis is done for the chosen model
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Chen, Qiong, and Mengxing Huang. "Rough fuzzy model based feature discretization in intelligent data preprocess." Journal of Cloud Computing 10, no. 1 (January 18, 2021). http://dx.doi.org/10.1186/s13677-020-00216-4.
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AbstractFeature discretization is an important preprocessing technology for massive data in industrial control. It improves the efficiency of edge-cloud computing by transforming continuous features into discrete ones, so as to meet the requirements of high-quality cloud services. Compared with other discretization methods, the discretization based on rough set has achieved good results in many applications because it can make full use of the known knowledge base without any prior information. However, the equivalence class of rough set is an ordinary set, which is difficult to describe the fuzzy components in the data, and the accuracy is low in some complex data types in big data environment. Therefore, we propose a rough fuzzy model based discretization algorithm (RFMD). Firstly, we use fuzzy c-means clustering to get the membership of each sample to each category. Then, we fuzzify the equivalence class of rough set by the obtained membership, and establish the fitness function of genetic algorithm based on rough fuzzy model to select the optimal discrete breakpoints on the continuous features. Finally, we compare the proposed method with the discretization algorithm based on rough set, the discretization algorithm based on information entropy, and the discretization algorithm based on chi-square test on remote sensing datasets. The experimental results verify the effectiveness of our method.